By allowing supervision, one can escape to some extent the limitations underlined by [1]}, trading off identifiability of the model with scalability to realistic settings. The latter is due to the absence of labelled data in real scenarios.
Supervision in disentanglement may come in different flavors, from weakly supervised settings [2]}, [3]}, to semi-supervised [4]} and fully supervised approaches [5]}.
We position our work in the weakly supervised setting; we assume to observe non i.i.d. sample pairs which differ only by a projection on a single submanifold.
